AI Impact Archives - GRMI

AI and Jobs in 2026: What Careers Are Safe — and What You Must Do Next

GRMI — Sat, 21 Mar 2026 05:24:49 +0000

AI Impact on Jobs: Careers That Remain Safe

Author: Anjori

As AI reshapes industries, professionals are asking which careers will thrive and which may disappear. This blog explores the impact of artificial intelligence on jobs, highlights roles that are less likely to be automated, and outlines the skills and strategies you need to stay relevant and future-ready in a rapidly evolving job market.

Artificial Intelligence and Jobs: What the Latest Research Really Indicates

Artificial Intelligence is reshaping workplaces across industries. However, recent research presents a more structured picture than public headlines often suggest.

Many discussions focus on job losses. Yet data highlights an important distinction between theoretical capability and actual real-world usage of AI systems.

Understanding this difference is essential for graduates and career switchers planning long-term professional paths.

What the Research Measured

A study analysing AI usage examined two dimensions:

Theoretical Capability: This refers to tasks AI systems can potentially perform.

Observed Real-World Usage: This measures how much AI is actually being used in workplaces today. The gap between these two figures is significant.

Where AI Shows the Highest Capability

According to the study:

Computer and Mathematics roles show approximately 94% theoretical exposure.
Office and Administrative roles show around 90% theoretical exposure.
Legal, Finance, Business, Engineering, and Management roles show over 60% theoretical exposure.

This indicates that many structured tasks in these sectors can technically be supported by AI tools.

However, capability does not automatically translate into full replacement.

What Is Actually Happening in Practice

When measuring real-world implementation:

Computer and Mathematics roles show around 33% observed automation.
Most other sectors remain below 20% actual AI usage.
Physical occupations such as construction and agriculture show near-zero exposure.

This clearly demonstrates that AI adoption is growing, but not uniformly across all industries.

The data suggests transformation rather than immediate elimination.

Which Types of Jobs Are More Exposed

Roles involving:

Repetitive workflows
Structured data processing
Template-based outputs
Rule-driven decision making

are more likely to experience automation support.

Examples include basic data entry, routine administrative coordination, script-based customer support, simple report generation, and entry-level coding assistance.

These functions can often be handled efficiently by AI systems because they follow predictable patterns.

Which Roles Remain Strongly Relevant

Professions requiring:

Strategy Analyst
Business Analyst (Transformation Role)
Management Consultant
Enterprise Risk Consultant
Corporate Governance Officer

AI tools may provide data insights, but these professionals interpret the results in the context of business goals, regulations, and market conditions.

Why These Remains Relevant

These roles are less dependent on repetitive execution and more focused on evaluation, coordination, and decision support.

They work across departments, analyse business performance, assess risks, and support leadership in planning long-term direction.

Consider the role of a management consultant.

A consultant is engaged when an organisation faces challenges such as declining performance, digital transformation needs, cost optimisation, or expansion into new markets.

Each organisation operates differently. Each challenge involves unique financial, operational, and cultural factors.

For example, if a company plans to implement AI tools, leadership must evaluate:

Implementation risks
Data security implications
Workforce adaptation
Regulatory compliance
Long-term strategic alignment

AI can generate insights, but it cannot understand organisational dynamics, stakeholder priorities, or executive responsibility independently.

Consultants analyse complex situations, interpret findings, communicate recommendations, and support leadership decisions.

These responsibilities require judgement, structured reasoning, and human accountability.

Therefore, consulting remains stable even in an AI-driven environment.

Why Risk Evaluation Becomes Even More Important

As organisations adopt AI systems, new risks emerge, including:

Operational risk
Cybersecurity risk
Model bias risk
Regulatory compliance risk
Data governance challenges

Risk associates and analysts play a vital role in identifying these uncertainties.

Their responsibilities include assessing impact, evaluating probability, designing mitigation strategies, supporting internal controls, and ensuring regulatory alignment.

While AI can assist in analysing data, it cannot independently take responsibility for risk decisions.

Risk management requires interpretation, scenario thinking, and accountability at the organisational level.

As technology expands, structured oversight becomes more significant rather than less relevant.

What This Means for Graduates and Career Switchers

The current workforce transformation does not indicate the disappearance of opportunities.

Instead, it signals a shift towards analytical, governance-oriented, and strategy-based roles.

Professionals who understand enterprise risk frameworks, regulatory structures, and organisational strategy will continue to add value.

Developing such competencies can provide long-term career stability in evolving industries.

Structured Preparation Through Risk Education

Building capability in risk-focused domains requires systematic learning.

The GRMI’s Post Graduate Programme in Risk Management is designed to develop understanding of enterprise risk concepts, financial risk principles, governance frameworks, regulatory awareness, and practical industry exposure.

Such structured education helps graduates prepare for roles in consulting, compliance, risk analysis, and corporate strategy.

Rather than competing with automation, students are equipped to oversee and guide responsible technology implementation within organisations.

In an AI-integrated economy, this alignment between risk knowledge and technological adoption becomes increasingly relevant.

Conclusion

Artificial Intelligence is transforming workplaces, particularly in structured and repetitive task-based roles.

However, real-world adoption remains gradual, and many responsibilities continue to depend on judgement, accountability, and strategic interpretation.

Consulting and risk management functions fall within this category.

For graduates and career switchers, understanding these trends can help in selecting future-oriented professional pathways.

Structured preparation in risk management, such as through GRMI, can support informed career development aligned with industry transformation.

FAQ's

Q1: Will AI completely replace jobs in the future?
Ans: AI will automate certain tasks, but most roles will evolve rather than disappear.

Q2: Which jobs are most affected by AI?
Ans: Roles involving repetitive and structured tasks are more likely to experience automation support.

Q3: Are consulting and risk management careers safe from AI?
Ans: Yes, because they require judgement, responsibility, and strategic decision-making.

Q4: Why is risk management important in an AI-driven company?
Ans: AI adoption introduces new operational, regulatory, and data risks that need structured oversight.

The post AI and Jobs in 2026: What Careers Are Safe — and What You Must Do Next appeared first on GRMI.

Artificial Intelligence and Jobs: What the Latest Research Really Indicates

GRMI — Thu, 19 Mar 2026 10:19:38 +0000

Artificial Intelligence and Jobs: What the Latest Research Really Indicates

Dr.Shalini Kumar
(Associate Director, Research & Global Accreditation)

Artificial Intelligence is reshaping workplaces across industries. However, recent research presents a more structured picture than public headlines often suggest.

Many discussions focus on job losses. Yet data highlights an important distinction between theoretical capability and actual real-world usage of AI systems.

Understanding this difference is essential for graduates and career switchers planning long-term professional paths.

What the Research Measured

The Indian government invoked regulatory powers to prioritise household LPG supply while attempting to stabilise domestic market conditions.

Refineries were instructed to maximise LPG production for households while temporarily reducing allocations for commercial sectors across India.

Daily monitoring, real-time oversight, and central control rooms were deployed to counter misinformation and panic about LPG shortages.

Authorities emphasised that households remained protected, aiming to reduce hoarding and maintain balance between supply and demand effectively.

Non-priority commercial sectors like restaurants and hotels faced restricted LPG allocations, highlighting operational risks for small businesses nationwide.

Where AI Shows the Highest Capability

According to the study:

Computer and Mathematics roles show approximately 94% theoretical exposure.
Office and Administrative roles show around 90% theoretical exposure.
Legal, Finance, Business, Engineering, and Management roles show over 60% theoretical exposure.

This indicates that many structured tasks in these sectors can technically be supported by AI tools.

However, capability does not automatically translate into full replacement.

What Is Actually Happening in Practice

When measuring real-world implementation:

Computer and Mathematics roles show around 33% observed automation.
Most other sectors remain below 20% actual AI usage.
Physical occupations such as construction and agriculture show near-zero exposure.

This clearly demonstrates that AI adoption is growing, but not uniformly across all industries.

The data suggests transformation rather than immediate elimination.

Which Types of Jobs Are More Exposed

Roles involving:

Repetitive workflows
Structured data processing
Template-based outputs
Rule-driven decision making

are more likely to experience automation support.

Examples include basic data entry, routine administrative coordination, script-based customer support, simple report generation, and entry-level coding assistance.

These functions can often be handled efficiently by AI systems because they follow predictable patterns.

Which Roles Remain Strongly Relevant

India’s LPG shortage highlights the urgent need to strengthen energy security and improve risk management strategies proactively and continuously.

Strategic planning, infrastructure upgrades, and policy interventions must work together to build more resilient LPG supply chains for the future.

Key Strategic Actions:

Diversify import sources: Secure alternative international LPG suppliers to reduce reliance on conflict-prone regions globally.
Increase domestic production: Expand refinery capacity to lower import dependence and enhance national energy self-reliance effectively.
Strategic stockpiling: Maintain emergency reserves at national and regional levels to buffer against supply shocks.
Strengthen supply chain monitoring: Implement predictive analytics and real-time tracking for improved decision-making during disruptions.
Policy and regulatory measures: Control allocations, prevent hoarding, and enforce price stability for households and commercial sectors.
Public awareness and communication: Educate consumers to reduce panic buying and encourage responsible usage during supply stress periods.

Why These Remains Relevant

These roles are less dependent on repetitive execution and more focused on evaluation, coordination, and decision support.

They work across departments, analyse business performance, assess risks, and support leadership in planning long-term direction.

Consider the role of a management consultant.

A consultant is engaged when an organisation faces challenges such as declining performance, digital transformation needs, cost optimisation, or expansion into new markets.

Each organisation operates differently. Each challenge involves unique financial, operational, and cultural factors.

For example, if a company plans to implement AI tools, leadership must evaluate:

Implementation risks
Data security implications
Workforce adaptation
Regulatory compliance
Long-term strategic alignment

AI can generate insights, but it cannot understand organisational dynamics, stakeholder priorities, or executive responsibility independently.

Consultants analyse complex situations, interpret findings, communicate recommendations, and support leadership decisions.

These responsibilities require judgement, structured reasoning, and human accountability.

Therefore, consulting remains stable even in an AI-driven environment.

Why Risk Evaluation Becomes Even More Important

As organisations adopt AI systems, new risks emerge, including:

Operational risk
Cybersecurity risk
Model bias risk
Regulatory compliance risk
Data governance challenges

Risk associates and analysts play a vital role in identifying these uncertainties.

Their responsibilities include assessing impact, evaluating probability, designing mitigation strategies, supporting internal controls, and ensuring regulatory alignment.

While AI can assist in analysing data, it cannot independently take responsibility for risk decisions.

Risk management requires interpretation, scenario thinking, and accountability at the organisational level.

As technology expands, structured oversight becomes more significant rather than less relevant.

What This Means for Graduates and Career Switchers

The current workforce transformation does not indicate the disappearance of opportunities.

Instead, it signals a shift towards analytical, governance-oriented, and strategy-based roles.

Professionals who understand enterprise risk frameworks, regulatory structures, and organisational strategy will continue to add value.

Developing such competencies can provide long-term career stability in evolving industries.

Structured Preparation Through Risk Education

Building capability in risk-focused domains requires systematic learning.

Such structured education helps graduates prepare for roles in consulting, compliance, risk analysis, and corporate strategy.

Rather than competing with automation, students are equipped to oversee and guide responsible technology implementation within organisations.

In an AI-integrated economy, this alignment between risk knowledge and technological adoption becomes increasingly relevant.

Conclusion

Artificial Intelligence is transforming workplaces, particularly in structured and repetitive task-based roles.

However, real-world adoption remains gradual, and many responsibilities continue to depend on judgement, accountability, and strategic interpretation.

Consulting and risk management functions fall within this category.

For graduates and career switchers, understanding these trends can help in selecting future-oriented professional pathways.

Structured preparation in risk management, such as through GRMI, can support informed career development aligned with industry transformation.

FAQ's

Will AI completely replace jobs in the future?
AI will automate certain tasks, but most roles will evolve rather than disappear.
Which jobs are most affected by AI?
Roles involving repetitive and structured tasks are more likely to experience automation support.
Are consulting and risk management careers safe from AI?
Yes, because they require judgement, responsibility, and strategic decision-making.
Why is risk management important in an AI-driven company?
AI adoption introduces new operational, regulatory, and data risks that need structured oversight.
How can graduates prepare for an AI-influenced job market?
By developing analytical skills and pursuing structured programmes like risk management education.

The post Artificial Intelligence and Jobs: What the Latest Research Really Indicates appeared first on GRMI.